@InProceedings{heinecke-asadullah:2017:K17-3,
  author    = {Heinecke, Johannes  and  Asadullah, Munshi},
  title     = {Multi-Model and Crosslingual Dependency Analysis},
  booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {111--118},
  abstract  = {This paper describes the system of the Team Orange-Deski\~{n}, used for the CoNLL
	2017 UD Shared Task in
	Multilingual Dependency Parsing. We based our approach on an existing open
	source tool (BistParser), which we modified in order to produce the required
	output. Additionally we added a kind of pseudo-projectivisation. This was
	needed since some of the task’s languages have a high percentage of
	non-projective dependency trees. In most cases we also employed word
	embeddings. For the 4 surprise languages, the data provided seemed too little
	to train on. Thus we decided to use the training data of typologically close
	languages instead. Our system achieved a macro-averaged LAS of 68.61% (10th in
	the overall ranking) which improved to 69.38% after bug fixes.},
  url       = {http://www.aclweb.org/anthology/K17-3011}
}